Our proposed region mask is a customized operation for unsupervised visual tracking in cycle training, which penalizes tracking errors on intermediate frames by making the coordinates differentiable.
As claimed in our manuscripts, when compared to conventional feature selection operation like PrPool [1]}, this operation is efficient to select features from last search region feature based on the output of region proposal network (RPN), denoted as \(P_{cls}\)  and \(P_{reg}\) .
Specifically, we set boxes number as 3125 (\(25 \times 25 \times 5\) ), the same as the total number of predicted boxes of RPN. And the positive threshold (denoted as \(TH\)  in the manuscript) is set as 0 for passing all predicted boxes.
Besides, we also visualize the regional mask propagation in training samples with different positive thresholds.
The regional masks on search region images with four different threshold values are shown in Fig REF .
When the value of this threshold increases, the region mask tend to filter out more predicted boxes with low confidence scores, which results in less information propagating between frames.
Based on this observation, we always set \(TH=0\)  in training phase, for propagating more information between frames. In addition, when proposal boxes with top confidence scores are wrong, region mask with a lower positive threshold is more likely to select the correct proposal box.
